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This article investigates the problem of estimating stellar atmosphericparameters from spectra. Feature extraction is a key procedure in estimatingstellar parameters automatically. We propose a scheme for spectral featureextraction and atmospheric parameter estimation using the following threeprocedures: firstly, learn a set of basic structure elements (BSE) from stellarspectra using an autoencoder; secondly, extract representative features fromstellar spectra based on the learned BSEs through some procedures ofconvolution and pooling; thirdly, estimate stellar parameters ($T_{eff}$,log$~g$, [Fe/H]) using a back-propagation (BP) network. The proposed scheme hasbeen evaluated on both real spectra from Sloan Digital Sky Survey (SDSS)/SloanExtension for Galactic Understanding and Exploration (SEGUE) and syntheticspectra calculated from Kurucz's new opacity distribution function (NEWODF)models. The best mean absolute errors (MAEs) are 0.0060 dex for log$~T_{eff}$,0.1978 dex for log$~g$ and 0.1770 dex for [Fe/H] for the real spectra and0.0004 dex for log$~T_{eff}$, 0.0145 dex for log$~g$ and 0.0070 dex for [Fe/H]for the synthetic spectra.

An autoencoder of stellar spectra and its application in automatically estimating atmospheric parameters